Spaces:
Running on Zero
Running on Zero
File size: 3,401 Bytes
6724737 95fec78 6724737 6661f3a 95fec78 6724737 6661f3a 95fec78 6724737 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | from __future__ import annotations
from typing import Any
from pozify.contracts import RepAnalysis
from pozify.exercises.base import ExerciseStrategy
from pozify.exercises.push_up.analyzer import PushUpAnalyzer
from pozify.exercises.shared.issue_marker import IssueRule
from pozify.exercises.push_up.issue_markers import RULES as ISSUE_RULES
from pozify.exercises.shared.rep_counter import combine, mean_optional, normalized_samples
from pozify.steps.rep_signals import SignalSample, angle_deg, average_axis, body_line_score
class PushUpExercise(PushUpAnalyzer, ExerciseStrategy):
exercise = "push_up"
def issue_rules(self) -> tuple[IssueRule, ...]:
return ISSUE_RULES
def build_signal(self) -> tuple[list[SignalSample], dict[str, Any]]:
sequence = self.pose_sequence
hip_y = [average_axis(frame, ("left_hip", "right_hip"), "y") for frame in sequence.frames]
shoulder_y = [average_axis(frame, ("left_shoulder", "right_shoulder"), "y") for frame in sequence.frames]
elbow_bend = [
mean_optional(
[
None if angle is None else max(0.0, 180.0 - angle)
for angle in (
angle_deg(frame, "left_shoulder", "left_elbow", "left_wrist"),
angle_deg(frame, "right_shoulder", "right_elbow", "right_wrist"),
)
]
)
for frame in sequence.frames
]
body_line = [body_line_score(frame) for frame in sequence.frames]
chest_proxy = [
mean_optional([shoulder_value, hip_value])
for shoulder_value, hip_value in zip(shoulder_y, hip_y, strict=False)
]
samples, signal_range = normalized_samples(sequence, combine(chest_proxy, elbow_bend, weight=0.25))
return samples, {
"selected_signal": "chest_y_plus_elbow_bend",
"raw_signal_range": signal_range,
"usable_signal_samples": len(samples),
"body_line_mean": round(mean_optional(body_line) or 0.0, 4),
}
def detect_variation(self, analysis: RepAnalysis) -> tuple[str, float, list[str]]:
hand_width = self.metric(analysis, "avg_hand_width_ratio")
knee_support = self.metric(analysis, "avg_knee_support_score")
not_issues: list[str] = []
if knee_support is not None and knee_support >= 0.8:
return "knee_push_up", self.confidence(0.74, analysis, knee_support), ["knee_contact"]
if hand_width is not None and hand_width >= 1.45:
return "wide_grip_push_up", self.confidence(0.72, analysis, hand_width), ["wide_hand_placement"]
if hand_width is not None and hand_width <= 0.95:
return (
"close_grip_push_up",
self.confidence(0.72, analysis, 1.0 - hand_width),
["close_hand_placement"],
)
if hand_width is None:
not_issues.append("hand_width_unverified")
return "standard_push_up", self.confidence(0.62, analysis, hand_width), not_issues
def profile_not_issues(self, variation: str) -> list[str]:
mapping = {
"wide_grip_push_up": ["wide_hand_placement"],
"close_grip_push_up": ["close_hand_placement"],
"knee_push_up": ["knee_contact"],
}
return list(mapping.get(variation, []))
|